Responses of the Soil Microbial Community to Weathering of Ore Minerals

R.L Simister, Department of Microbiology and Immunology, and Mineral Deposits Research Unit (MDRU),

Department of Earth, Ocean and Atmospheric Sciences, The University of British Columbia, Vancouver, BC

P.A. Winterburn, Mineral Deposits Research Unit (MDRU), Department of Earth, Ocean and Atmospheric

Sciences, The University of British Columbia, Vancouver, BC

S.A. Crowe, Departments of Microbiology and Immunology, and Earth, Ocean and Atmospheric Sciences, The

University of British Columbia, Vancouver, BC, sean.crowe@ubc.ca (corresponding author)

Simister, R.L., Winterburn, P.A. and Crowe, S.A. (2018): Responses of the soil microbial community to weathering of ore minerals; inGeoscience BC Summary of Activities 2017: Minerals and Mining, Geoscience BC, Report 2018-1, p. 57–68 .

Introduction

As global population grows and modernizes, demand for

mineral resources is expanding (Kesler, 2007). At the same

time, existing orebodies are being exhausted, while the fre-

quency of new discoveries of exposed or partially exposed

deposits diminishes. Demand for mineral resources must

therefore be met through the discovery and development of

buried or concealed mineral deposits. Although mineral re-

source extraction supported the core of the Canadian econ-

omy for over a century—currently contributing $56 billion

to Canada’s GDP and providing 19% of its goods exports

(The Mining Association of Canada, 2017)—its ability to

do so relies on continued discovery of mineral deposits that

may be concealed by overburden. Finding these mineral

deposits beneath exotic overburden consisting of glacial

and preglacial sediments remains a fundamental and wide-

spread challenge to mineral exploration in Canada (Ander-

son et al., 2012; Ferbey et al., 2014).

New and innovative techniques that complement, enhance

or even surpass traditional techniques to define the surface

expression of buried ore mineralization could minimize the

cost of exploration and help in targeting drilling activities

(Kelley et al., 2006). Several recent studies in British Co-

lumbia (BC) have demonstrated the potential for new sur-

face geochemical techniques to lead to the discovery of

concealed orebodies. These include indicator minerals

(Plouffe et al., 2013a, b), soil partial leach and selective ex-

traction geochemistry on multiple soil horizons (Van

Geffen et al., 2009; Bissig and Riquelme, 2010; Heberlein

and Samson, 2010), halogen element detection (e.g.,

Heberlein et al., 2017), till geochemistry (Cook et al., 1995)

and biogeochemistry (Dunn, 1986; Reid and Hill, 2010).

Each geochemical technique and media type has both

strengths and weaknesses in identifying buried mineraliza-

tion:

• Indicator minerals (e.g., Plouffe et al., 2013a; Plouffe

and Ferbey, 2016) and biogeochemistry (e.g., Dunn et

al., 2015; Jackaman and Sacco, 2016) have demon-

strated success in targeting at a regional reconnaissance

scale, but additional tools are still required to define fi-

nal drill targets.

• Surface geochemical techniques (e.g., soil and till) for

near-source detection have not reached a level of robust-

ness to generate high-confidence drill targets. Specifi-

cally, geochemical signatures generated from orienta-

tion surveys over known mineral deposits are noisy (i.e.,

poor resolution of anomalies against background; Stan-

ley, 2003), show poor precision and have element pat-

terns that are often difficult to reconcile with mineral-

deposit chemistry and expected element mobility

(Heberlein and Samson, 2010).

• Unfortunately for mineral explorers, published research

has led to marketing of a range of competitive commer-

cial analytical methodologies loosely grouped as ‘selec-

tive or partial extraction techniques’, many of which are

proprietary to specific companies. The interpretation of

the data is often ambiguous, especially if it is under-

taken without consideration of the heterogeneity of the

sampled mineralogy, organic-matter character, element

dispersion and host (Cameron et al., 2004; Anand et al.,

2016).

• Organic geochemical techniques for direct detection of

deposits are dominated by the proprietary Spatiotem-

poral Geochemical Hydrocarbons (SGH®) method. As

with the selective extractions, application of the SGH®

technique is dominated by the junior exploration indus-

try. The major exploration companies generally do not

apply the technique due to concerns with robustness, re-

peatability of survey results and lack of a demonstrable

link between the compounds analyzed and the mineral-

ization at depth (Noble et al., 2013). There is effectively

no fundamental understanding of how and where the

hydrocarbon signatures are generated.

Geoscience BC Report 2018-1 57

This publication is also available, free of charge, as colour digitalfiles in Adobe Acrobat® PDF format from the Geoscience BC web-site: http://www.geosciencebc.com/s/SummaryofActivities.asp.

The lack of fundamental mechanistic understanding of

these techniques beyond their broadest concepts has led to

inappropriate application by the mineral exploration indus-

try with minimal return on investment. The failure of the

commercial techniques to repeat the performance shown in

orientation surveys over known mineralization has, in large

part, resulted in their abandonment by major exploration

companies. Despite these issues, there is sufficient empiri-

cal evidence to indicate causative links between mineral-

ization beneath transported cover and the presence of geo-

chemical gradients in the surface environment (Hamilton,

1998; Smee, 1998; Kelley et al., 2006; Nordstrom, 2011).

Although much less explored, biological anomalies may be

more robust indicators of buried mineralization (Kelley et

al., 2006; Leslie et al. 2013), and such anomalies may be de-

tectable through low-cost, high-throughput geobiological1

surveys.

Microbial-Community Fingerprinting as aMineral Exploration Tool

Micro-organisms kinetically enhance and exploit thermo-

dynamically favourable geochemical reactions, including

the dissolution and formation of diverse minerals, to sup-

port their metabolism and growth in nearly every low-tem-

perature geological setting (Newman and Banfield, 2002;

Falkowski et al., 2008). They are acutely sensitive, often

rapidly responding to the dynamics of chemical and physi-

cal gradients in the environment. Subtle changes in mineral

bioavailability, for example, can be reflected in dramatic

shifts in composition and activity of the microbial commu-

nity (Newman and Banfield, 2002; Fierer, 2017). This can

be seen at the global scale as marine phytoplankton com-

munities respond to traces of iron in seawater, a process that

can be viewed as chlorophyll plumes via remote sensing

(O’Reilly et al., 1998; Fuhrman et al., 2008). Application of

modern sequencing technologies allows high-throughput

profiling of the taxonomic diversity and metabolic poten-

tial of soil microbial communities across subtle, and often

poorly resolved, geochemical gradients.

Microbial-community profiles thus have a strong potential

to resolve chemical and physical differences in sample suites

that are not readily discernible through conventional geo-

chemical and geophysical surveys. In residual terrains, for

example, where chemical gradients are high, bacterial pop-

ulation changes have been clearly demonstrated (e.g.,

Southam and Saunders, 2005; Reith and Rogers, 2008).

Even outdated techniques with low throughput and resolu-

tion, such as Denaturing Gradient Gel Electrophoresis

(DGGE), that can produce a crude microbial-community

“fingerprint” (Wakelin et al., 2012) reveal changes in bac-

terial communities in soils over buried volcanogenic mas-

sive-sulphide (VMS) deposits. The advent of high-

throughput next-generation sequencing (NGS) platforms

during the last decade has transformed our capacity to inter-

rogate the molecular fingerprints of microbial communities

(Binladen et al., 2007; Shokralla et al., 2012; Zhou et al.,

2015). Application of NGS technologies thus allows profil-

ing of the taxonomic diversity and metabolic potential of

soil microbial communities across defined survey areas.

Given that each soil sample comprises thousands of micro-

bial taxa, each containing hundreds to thousands of genes

(Fierer, 2017), the statistical power of this approach to

identify anomalies is unprecedented. A schematic diagram

of such an approach is illustrated in Figure 1.

To enhance the ability to recognize microbial fingerprints

in the surface environment related to buried mineralization,

a laboratory experiment has been conducted in which back-

ground soils were either amended with the copper-bearing

mineral chalcopyrite or doped with copper as copper sul-

phate (CuSO4). These soils were then incubated to test the

response of the microbial community to the presence of

copper amendments. Some organisms have evolved dis-

tinct extracellular acquisition and internal storage strate-

gies to target elements that are specifically required for en-

zymatic or metabolic processes (Liermann et al., 2007), and

the requirement for copper in some microbial species has

been well documented in controlled studies (Knapp et al.,

2007; Fru et al., 2011; Balasubramanian et al., 2012; Ken-

ney and Rosenzweig, 2012). Since both chalcopyrite

weathering in soils and soil microbial turnover are appre-

ciable over timescales of several weeks, these experiments

are traceable in the laboratory (Whitman et al., 1998;

Kimball et al., 2010). The composition of the soil microbial

community has been analyzed at initial, intermediate and

end time-points, allowing identification of members of the

soil microbial community that respond to the presence of

ore minerals. These first bench-scale results will facilitate

more detailed and controlled tests, in the future, for the

presence or abundance of specific community members

and their metabolic capacity in relation to buried mineral

deposits.

Methodology

Soil and Ore Amendment

An archived soil sample from close to the Deerhorn por-

phyry, located 70 km northeast of Williams Lake in central

BC, was retrieved (sample number 282140 of Rich, 2016).

This sample is considered as representative of background

because it has insignificant base-metal contents. The sam-

ple was collected from the upper B horizon under aseptic

conditions and screened to –6 mm in the field prior to stor-

age at ambient temperatures in double-sealed zip-lock

bags. The sample was digested using a multi-acid digestion

58 Geoscience BC Summary of Activities 2017: Minerals and Mining

1Geobiology is the interdisciplinary science dealing with theinteraction between organisms and ecosystems and theirphysical environment (Oxford University Press, 2017).

and the digestate analyzed by inductively coupled plasma–

mass spectrometry (ICP-MS) to determine that the soil con-

tains 6 ppm Cu, 1 ppm As and 0.32 ppm Mo. The soil was

not dried prior to the start of the experiment. Soil was dis-

pensed aseptically into sterile containers for each treat-

ment, with amendment concentrations chosen to represent

either concentrations of copper that are routinely detected

in geochemical surveys over buried mineral deposits (am-

bient or ‘(Am)’) or very high levels of copper that might be

expected in highly anomalous soils (high or ‘(Hi)’). The

amendments were as follows: 1) ‘Hi-ore’soil was amended

with chalcopyrite ore at 600 ppm Cu; 2) ‘Am-ore’ soil was

amended with chalcopyrite ore at 200 ppm Cu; 3) ‘Hi-Cu’

soil was amended with copper in the form of CuSO4 (dis-

solved in Milli-Q®-filtered water) to 600 ppm Cu; and

4) ‘Am-Cu’ soil was amended with copper in the form of

CuSO4 to 200 ppm Cu. Soil was sampled at T = 0, T = 1

(2 weeks) and T = 2 (5 weeks).

DNA Extraction

Microbial-community DNA was extracted from samples

using a MO BIO Laboratories Inc. PowerMax® Soil DNA

Isolation Kit; as per manufacturer’s instructions, approxi-

mately 0.25 g of soil was used. Resulting DNA was stored

at –20°C. The quality and quantity of genomic DNA were

measured on a ThermoFisher Scientific NanoDrop® ND-

1000 spectrophotometer and by using Invitrogen™

PicoGreen™ (Quant-iT™ dsDNA Assay Kit) dye.

Geoscience BC Report 2018-1 59

Figure 1. Schematic diagram of microbial fingerprinting applied to mineral deposit exploration. DNA is extracted and purified from soil sam-ples taken in geobiological surveys and then sequenced to 1) generate iTag libraries of the 16S rRNA gene for community fingerprintinganalysis, and 2) generate metagenomes to mechanistically link anomalous microbial communities to underlying differences in coded meta-bolic potential. These anomalies, reflected by hundreds to thousands of microbial species, will form unique fingerprints or barcodes that arecharacteristic of proximity to buried mineral resources. These barcodes will be formatted into data products such as deposit-scale explora-tion maps that chart microbial fingerprints (operational taxonomic units [OTUs], indicator and clustering analysis) and anomalies/ePDGBs(environmental pathway genome databases) specifically linked to mineral deposits.

Small Subunit Ribosomal RNA (SSU rRNA)Gene Amplification and iTag Sequencing

Bacterial and archaeal 16S rRNA gene fragments from the

extracted genomic DNAwere amplified using primers 515f

and 806r (Apprill et al., 2015). Sample preparation for

amplicon sequencing was performed as described by Koz-

ich et al. (2013). In brief, the aforementioned 16S rRNA

gene-targeting primers, complete with Illumina adapter, an

8-nucleotide index sequence, a 10-nucleotide pad se-

quence, a 2-nucleotide linker and the gene-specific primer

were used in equimolar concentrations together with

Deoxynucleotide triphosphate (dNTPs), Polymerase chain

reaction (PCR) buffer, MgSo4, 2U/µL ThermoFisher high-

fidelity platinum Taq DNA polymerase and PCR-certified

water to a final volume of 50 ìL. PCR amplification was

performed with an initial denaturing step of 95°C for 2 min,

followed by 30 cycles of denaturation (95°C for 20 s), an-

nealing (55°C for 15 s) and elongation (72°C for 5 min),

with a final elongation step at 72°C for 10 min. Equimolar

concentrations of amplicons were pooled into a single li-

brary. The amplicon library was analyzed on an Agilent

Bioanalyzer using the High-Sensitivity DS DNA Assay to

determine approximate library fragment size, and to verify

library integrity. Library pools were diluted to 4 nM and de-

natured into single strands using fresh 0.2 N NaOH, as

recommended by Illumina. The final library was loaded at a

concentration of 8 pM, with an additional PhiX spike-in of

5–20%. Sequencing was conducted on the MiSeq platform

at the Sequencing + Bioinformatics Consortium, The

University of British Columbia, Vancouver, BC (The

University of British Columbia, 2017).

Informatics

Sequences were processed using mothur (Schloss et al.,

2009, Kozich et al., 2013). Briefly, sequences were re-

moved from the analysis if they contained ambiguous char-

acters, had homopolymers longer than 8 base pairs and did

not align to a reference alignment of the correct sequencing

region. Unique sequences, and their frequency in each sam-

ple, were identified and then a pre-clustering algorithm was

used to further de-noise sequences within each sample

(Schloss et al., 2011). Unique sequences were identified

and aligned against a SILVA alignment (mothur Project,

2017a). Sequences were chimera-checked using VSEARCH

(Rognes et al., 2016) and reads were then clustered into

97% operational taxonomic units (OTUs) based on uncor-

rected pairwise distance matrices. OTUs were classified

60 Geoscience BC Summary of Activities 2017: Minerals and Mining

Figure 2. Bacterial diversity of samples. Rarefaction curves are based on operational taxonomic units (OTUs) at 97% sequence similarity.

using the SILVA reference taxonomy database (re-

lease 128; mothur Project, 2017b).

Results and Discussion

Soil is one of the most complex and diverse microbial habi-

tats, with merely 1 g containing up to 1010 cells and 104 bac-

terial species (Roesch et al., 2007; Torsvik and Øvreås,

2002). The current study’s approach relies on the ability to

capture this diversity through next-generation sequencing

technologies. In microbiology, the assessment of diversity

often involves calculation of species richness (number of

species present in a sample; Magurran, 2013). The most

common approach is to assign 16S rRNA sequences into

operational taxonomic units (OTUs) and represent these as

rarefaction curves, which plot the cumulative number of

OTUs captured as a function of sampling effort, and there-

fore indicate the OTU richness in a given set of samples.

Other common methods include nonparametric analysis,

such as Chao1, which estimates the overall sample diver-

sity (also known as alpha diversity; Hughes et al., 2001).

The current study extracted microbial-community DNA

from the soils amended with either chalcopyrite ore or cop-

per, and sequenced the 16S rRNA gene. Analysis of theses

sequences reveals that the number of observed OTUs (here-

after referred to as species) is 2265 ±105 (range 1993–

2380), with an alpha diversity (Chao1 index) of 3438 ±327

(range 2808–3791; Table 1), indicating that the sequencing

coverage was sufficient to capture 65% of the microbial-

community diversity. These levels of diversity are well in

line with diversity commonly observed in soils (Thompson

et al., 2017). These measurements dispel dogma that ex-

tremely high diversity in soil microbial communities ren-

ders them intractable to molecular-based microbial-com-

munity analysis. Rarefaction analysis revealed that

resampling of the observed OTUs approaches asymptotic

values (Figure 2), confirming adequate coverage for diver-

sity estimation. There was no pronounced difference in

species richness (i.e., the number of species in a given sam-

ple) over time, due to amendment with chalcopyrite ore or

copper. The study’s first measurements demonstrate that

soil diversity can be captured through next-generation

sequencing technologies, which bodes well for the

approach of imparting enormous statistical power to com-

munity profiles as anomaly indicators.

The number of reads per microbial phylum was normalized

to total read number for a given sample and expressed as a

percentage of the total reads from that sample (Figure 3).

Most microbial-community members belong to the Proteo-

bacteria (24–37%), Acidobacteria (13–32%) and Verruco-

microbia (11–21%) phyla (Figure 3). The relative propor-

tions are consistent with previous studies on soil

ecosystems (Choi et al., 2016; Kaiser et al., 2016). This

high-level taxonomic analysis reveals strong similarities

across all samples, thus giving confidence that the analyses

are not overwhelmed by intersample variability arising be-

cause of the very high levels of microbial diversity and

chemical and physical heterogeneity commonly found in

soils. The similarity across the samples, however, suggests

that discrimination between background and anomalous

soils may be more sensitive with analyses at the genus or

species level rather than at the phylum level. Nevertheless,

when plotted relative to the unamended (control) samples,

subtle changes in community composition through time

can be detected even at the phylum level (Figure 4). This

high-level sensitivity bodes well for application to explora-

tion.

Differences between copper-amended and chalcopyrite

ore–amended soils included a higher abundance of Chloro-

flexi in copper-treated soils at T1 and T2 (Figure 4A). The

Archaeal phylum Thaumarchaeota increased in abundance

relative to the control in samples amended with high levels

of chalcopyrite ore (Hi-Ore) and copper (Hi-Cu; Fig-

ure 4B). The other phylum that increased over time in re-

sponse to soil amendments was the Firmicutes (Figure 4C).

All amendments elicited a decrease in the relative abun-

dance of Acidobacteria, Ignavibacteria and Bacteroidetes

(except for soils treated with ambient levels of chalcopyrite

ore [Am-ore]) compared to control soil over time (Fig-

ure 4D–F). Relationships between treatment type (chalco-

pyrite ore or copper) and time point (T = 0, 1, 2) were evalu-

ated through hierarchical-clustering analysis (Figure 5A).

All control samples clustered tightly, confirming similar

microbial-community compositions. Treated samples

grouped apart from controls, indicating that chalcopyrite

ore and copper amendments changed the composition of

the microbial community and that this change was easily re-

solvable through standard hierarchical-clustering analysis.

Hierarchal clustering separated chalcopyrite ore– and cop-

per-treated samples, indicating that it may be possible to

Geoscience BC Report 2018-1 61

Table 1. Overview of the species estimates and di-versity metrics obtained per sample after quality fil-tering. Sample names explained in ‘Soil and OreAmendment’ section. Abbreviation: OTU, opera-tional taxonomic unit.

determine microbial-community response to individual

metals.

A number of species were appreciably enriched or depleted

in response to chalcopyrite ore or copper amendment, so

the relative abundance of individual species normalized to

the relative abundance of the same species in the controls

was plotted versus time (examples shown in Figure 5B).

The species that increased in response to chalcopyrite ore

and copper amendment relative to controls included Rho-

danobacteria sp. (Koh et al., 2015), SC-I-84 sp. (Huaidong

et al., 2017) and Acidimicrobiales sp. (Figure 5B; Hallberg

et al., 2006). These species have frequently been found in

relatively high abundances in materials recovered from

acidic waters, sulphidic mine wastes and other mine-re-

lated environments, as well as acidic biofilms (Hallberg et

al., 2006; Stackebrandt, 2014; Koh et al., 2015; Huaidong

et al., 2017), anecdotally suggesting a link between the

ecology of these species and the concentration of metals in

their habitat. In addition to the broader community-level re-

sponses revealed through hierarchical clustering analyses,

the data from this study thus imply that certain species in

soil microbial communities may be useful as indicators of

exposure to ore components.

Conclusions and Future Directions

This study investigated the use of soil microbial-commu-

nity fingerprinting with modern DNA sequencing technol-

ogies to detect changes in soil microbial communities in re-

sponse to varying levels of exposure to chalcopyrite ore and

copper. It was found that soil microbial communities can be

coherently sampled such that there is little variability be-

tween samples. Exposure of soil microbial communities to

ore constituents elicits a response detectable on laboratory

time scales of several weeks. These responses are readily

resolved through standard statistical analyses, and the spe-

cific species that exhibited the strongest responses have

known affinities for environments rich in heavy metals.

The strong microbial responses observed are encouraging

signs for the use of microbial-community fingerprinting in

mineral deposit exploration. Further experiments are cur-

rently being conducted and work is ongoing to translate the

approach to a real-world exploration setting. With the co-

operation and permission of Consolidated Woodjam Cop-

per Corporation, the authors have collected a suite of

150 soil samples over known copper-gold porphyry miner-

alization (the Deerhorn deposit) in central BC, which has

62 Geoscience BC Summary of Activities 2017: Minerals and Mining

Figure 3. Distribution of 16S rRNA reads per phylum for each sample. The number of reads per phylum is calculated as a percentage of thetotal reads for each sample. The ‘*other’ grouping represents summed phyla that individually contributed <0.4% of the total number of readsper sample.

Geoscience BC Report 2018-1 63

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extensive geological, pedological, geochemical and geo-

physical metadata (Rich, 2016). Genomic DNA from

nearly half of the soils collected has been extracted and tag

sequencing of the 16S rRNA completed.

The first analyses of these data reveal strong similarities

across the entire sample set, lending confidence to the abil-

ity to consistently sample microbial communities from the

same horizon to yield a dataset from which robust compari-

sons can be made (Figure 6). Ongoing work includes con-

ducting statistical analyses (hierarchical clustering and in-

dicator-species analysis) to resolve possible patterns in the

microbial-community fingerprints that could point to

buried mineralization.

Acknowledgments

The authors thank S. Rich for sample collection, and

P. Kenward and D. Fowle for peer review of this paper.

Funding was provided by Geoscience BC.

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